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        <title>CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets</title>
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        <namePart type="given">Orest</namePart>
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            <title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop &amp; Shared Task</title>
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            <namePart type="given">Graciela</namePart>
            <namePart type="family">Gonzalez-Hernandez</namePart>
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            <namePart type="given">Davy</namePart>
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    <abstract>The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 (#SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model’s capabilities.</abstract>
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%0 Conference Proceedings
%T CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets
%A Xherija, Orest
%A Choi, Hojoon
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F xherija-choi-2022-complx
%X The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 (#SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model’s capabilities.
%U https://aclanthology.org/2022.smm4h-1.47
%P 176-181

Markdown (Informal)

[CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets](https://aclanthology.org/2022.smm4h-1.47) (Xherija & Choi, SMM4H 2022)

• CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets (Xherija & Choi, SMM4H 2022)

ACL

• Orest Xherija and Hojoon Choi. 2022. CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 176–181, Gyeongju, Republic of Korea. Association for Computational Linguistics.